Case Study - Renewable Energy Generating Potential of a City

Researchers at the University of Leeds have used modelling techniques to map and evaluate the renewable energy potential within the city of Leeds. Leeds City Council (LCC) have worked collaboratively with Professor Alison Tomlin and her team (Energy Research Institute, University of Leeds) to assess the potential for LCC to reduce their carbon footprint and energy bills though renewable energy installation, with encouraging results.

There are a number of factors that will influence the efficiency and financial success of any renewable energy installation. Government renewable energy incentives such as feed in tariffs (FIT) may make the installation of cheap domestic wind turbines and solar panels seem attractive, but the complexity of the shape of a city and variability in available wind energy and solar radiation make the viability of these investments less certain, leaving cities a large and virtually untapped energy resource.

Initial studies by the team focused on mapping the wind energy potential of five city regions [1,2].

A wind assessment model was developed based on a downscaling technique using high resolution LiDAR data to show building heights and vegetation, and Met Office wind speed data for the input climatology, making it possible to create a 3D map of wind speeds across an urban area. This model allowed identification of the best locations to exploit available wind energy. This has subsequently been developed into a software tool to inform people how much energy can be produced from a particular wind turbine near their home or workplace. It is hoped that this may become available as an online resource in the future.

Interestingly, in a case study of Leeds, it was found using this model that there many locations that may be viable for building mounted wind turbines, and some urban above-roof locations were shown to be comparable in performance to turbines located in well exposed rural sites [1].

Alison and the team wanted to take this work further to provide a useful resource incorporating solar mapping and government incentive analysis such as FIT. Thus providing a comprehensive tool to simply and cost effectively determine the total renewable generation capacity of a city region; including optimal locations for installations, the financial returns for different investment/ incentive scenarios, CO2 savings and whether a location favours a wind/ solar installation.

In the solar mapping model, LiDAR data was used in conjunction with surface analysis tools in ArcGIS (ESRI 2014) to analyse slope and aspect data across a city. Templates of standard roof shapes were created to simplify analysis of smaller properties. These data were combined with an area solar radiation tool (ESRI 2014) to understand the best locations of spaces and roof areas with optimum conditions for insolation, whilst accounting for shading effects from other buildings and terrain features [3].

The outputs of the combined model enable large asset holders, such as local authorities, to make strategic investment decisions based on technology feasibility estimations across their entire portfolio that are accurate at the individual asset scale. Such outputs could also better inform policy development by offering an alternative to regional top-down feasibility studies based on generalised socio-economic trends which, by their nature, cannot be inspected to the individual property level.

For this project, the results suggest significant potential for small-scale wind and solar power generation across the Leeds City Council portfolio, where a number of sites create a compelling business case for investment. Comparing financial payback times for optimal systems falling under the UK feed in tariff system showed that the best performing wind energy sites outperformed the best solar sites [4].

In all cases, using the generated electricity on site, rather than exporting back to the grid, improves the financial viability of an installation, suggesting that early investment decisions should focus on high demand buildings or electric vehicle charging[4].